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Rare-event sampling
Adaptive simulation strategies for efficiently discovering infrequent molecular transitions.
Computational Biophysics · Molecular Simulation · Machine Learning
Dibyendu Maity
I develop machine-learning-guided methods for discovering rare molecular events, reconstructing transition pathways, and extracting kinetics from molecular simulations.
- Ph.D. in Physics (Theoretical) — University of Calcutta / S. N. Bose National Centre for Basic Sciences
- Ph.D. thesis submitted
- Available for postdoctoral research positions from late 2026.
01 Free-energy landscape
Research
A synthesis of the research profile
My research develops and applies machine-learning-guided methods for sampling and characterising rare events in molecular systems, with emphasis on adaptive and weighted-ensemble sampling strategies for transition-path discovery and pathway-resolved kinetics.
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Transition pathways & kinetics
Pathway discovery, pathway-resolved analysis, weighted-ensemble simulations, fluxes, MFPTs, and kinetic observables.
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Machine-learned molecular representations
Autoencoders, variational autoencoders, learned collective variables, dimensionality reduction, and data-driven descriptors.
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Molecular & materials simulation
Applications spanning proteins, molecular recognition, phase transitions, molecular materials, solvation, and related problems.
Featured research
Methodological contributions
PathGennie
Direction-guided adaptive sampling for rare-event transition pathways
- Problem
- Rare molecular transitions — folding events, phase changes, chemical reorganizations — occur on timescales far beyond what direct molecular dynamics can reach, making transition pathways difficult to sample efficiently.
- Approach
- PathGennie uses ultrashort monitored trajectories to steer adaptive sampling along a direction that biases exploration toward the transition, generating rare-event pathways rapidly without long unbiased runs.
- Contribution
- Provides a practical, direction-guided adaptive-sampling scheme for generating transition pathways at substantially reduced computational cost, released as an open-source Python framework.
IceCoder
Variational-autoencoder representation learning for ice-phase identification
- Problem
- Distinguishing ice polymorphs and liquid environments from local molecular geometry is difficult with hand-crafted order parameters, which are often specific to a single phase or system.
- Approach
- IceCoder learns a compact latent representation of local molecular environments with a variational autoencoder, trained without phase labels, so that structurally distinct environments separate naturally in latent space.
- Contribution
- Delivers an unsupervised classifier for ice phases and liquid environments that generalizes across simulation conditions, packaged as an open-source PyTorch framework.
Pathway-resolved kinetics
Neural-network-guided weighted-ensemble simulations for channel-specific rates
- Problem
- When a rare event proceeds through multiple competing pathways, standard weighted-ensemble kinetics estimates the overall rate but obscures how much flux flows through each individual channel.
- Approach
- Candidate transition routes are grouped and refined into smooth reference pathways described by a neural-network path collective variable (PathCV), and independent weighted-ensemble (WE) simulations are then run along each channel.
- Contribution
- Enables quantitative, pathway-resolved kinetics — channel-specific fluxes and mean first-passage times — from weighted-ensemble simulations rather than a single lumped rate.
Selected publications
Recent work
- 2026
CoWERA: A Temporal Coherence Guided Binless Resampling Algorithm for Weighted-Ensemble Based Estimation of Rare-Event Kinetics
The Journal of Chemical Physics, 164, 134111
- 2026
Pathway Controlled Phase Separation of Minimal Building Blocks Utilizing a Dissociative Chemical Transformation
Angewandte Chemie International Edition, e1914460
- 2026
Microwave-Assisted Thermal Profiling of Blood: A Potential Biomarker for Differentiating Cancer and Non-Cancer States
Journal of Medical Engineering & Technology, 1–16
- 2026
Quantitative Pathway-Resolved Kinetics from Neural Network-Guided Weighted Ensemble Simulations
ChemRxiv Preprint
- 2025
No publications match the current filters.
Scientific software
Open-source research tools
Software developed to make rare-event sampling and representation-learning methods usable in ordinary molecular-dynamics and materials-simulation workflows.
TRAILS-MD
PythonLightweight, engine-agnostic framework for lineage-aware adaptive molecular-dynamics sampling.
View repositoryPathGennie
PythonDirection-guided adaptive-sampling framework for rapidly generating rare-event transition pathways.
View repositoryIceCoder
Python / PyTorchUnsupervised representation-learning framework for classification and identification of ice polymorphs and liquid environments.
View repositorySolOrder
Python / C++Local solvation and structural-order-parameter analysis utilities for molecular simulations.
View repositoryAcademic progression
Timeline
- 2016–2019
B.Sc. in Physics (Honours)
Midnapore College, West Bengal
- 2019–2021
M.Sc. in Physical Sciences
University of Calcutta / S. N. Bose National Centre for Basic Sciences, Kolkata
- 2021–2026
Ph.D. in Physics (Theoretical)
University of Calcutta / S. N. Bose National Centre for Basic Sciences, Kolkata
Advisor: Prof. Suman Chakrabarty
Thesis: Development and Application of Machine Learning Approaches for Prediction, Identification and Sampling Problems in the Field of Molecular Modeling and Simulation
Thesis submitted
Get in touch
Research, collaboration, and postdoctoral opportunities
Available for postdoctoral research positions from late 2026.